The Queensland Cadastral Survey Requirements gives clear guidelines as to the calculation and validation of Positional Uncertainty (PU) and Survey Uncertainty (SU)when using RTK GNSS observations. The purpose of this paper is to give some background and guidance for calculating uncertainty for terrestrial methods

Campbell, Glenn

University of Southern Queensland ePrints

    May 2018             CALCULATING POSITIONAL AND SURVEY UNCERTAINTY FOR TERRESTRIAL OBSERVATIONS Glenn Campbell Calculating Positional and Survey Uncertainty for Terrestrial Observations 1   Background In Queensland the Survey and Mapping Infrastructure Act 2003 gives the Chief Executive of the   Department of Natural Resources and Mines and Energy the power to makes standards and guidelines in relation to the performance of cadastral surveys. Two of the standards (3.14.3 and 3.28.1 in the Cadastral Survey Requirements) rely on the concepts of survey uncertainty (SU), positional uncertainty (PU) and relative uncertainty (RU). SU represents the uncertainty in control mark co-ordinates at the 95% confidence level free from the influence of any imprecision or inaccuracy in the underlying datum realisation. PU is the SU with the addition of the uncertainty in the datum realisation and RU is the uncertainty of the join between two control marks (for more detailed definitions see p5 of the ICSM’s Special Publication 1 (SP1) https://www.icsm.gov.au/publications/standard-australian-survey-control-network-v21). The Cadastral Survey Requirements gives clear guidelines as to the calculation and validation of these values when using RTK GNSS observations (see s8.4). The purpose of this paper is to give some background and guidance for calculating uncertainty for terrestrial methods.  Error Propagation The law of propagation of errors is concerned with the behaviour of random errors propagating through a measurement system. Linear Problems When the final value is created by the linear addition of independent measurements the error associated with it takes on the simplest form; 12 21( )nX ii         (1)       where σx is the standard deviation of the result and σi are the standard deviations of the n independent measurements.   To be less mathematical the σx is obtained by taking the square root of the sum of the variances of the individual measurements (for a measurement the standard deviation is the square root of the variance).  Example 1 The total distance A−D and its standard deviation is required. The line was measured in three independent sections as follows: AB 51.00 m ± 0.05 m BC 36.50 m ± 0.04 m CD 26.75 m ± 0.03 m  The total distance of AD = AB + BC + CD  = 51.00 + 36.50 + 26.75 = 114.25  Calculating Positional and Survey Uncertainty for Terrestrial Observations 2   From the law of propagation of errors      2 2 22 2 2 22                     0.05  0.04  0.03       0.005    0.071AD CDADACDAB B         Uncorrelated Non-Linear Problems More often than not in surveying individual measurements need to be combined to get the desired parameter. A common example is calculating a horizontal distance from a slope distance and vertical circle reading.      sinh s        (2) where h is the horizontal distance, s measured slope distance and  is the measured zenith distance (if using the vertical angle then the trig function changes to cos). Rather than the simple addition from Example 1 the measurements are being combine non-linearly.     In general terms, if y represents a quantity computed from several measurements (random variables) represented by x1, x2, ..., xn in a non-linear function y = f (x1, x2, ..., xn) and if σ1, σ2, ..., σn represent the standard deviations of the measurements x1, x2, ..., xn which are assumed to be independent. Then, σy is computed by 22 21iny xi iyx           (3)  Example 2 A horizontal distance needs to be calculated from a slope distance is s = 100.00 m with σs = 0.05 m and β = 85°00' with σβ = 00°30'. Compute h and σh. (Assume s and β to be uncorrelated.)      222 2 222 2 2 2sin (100.00)(0.996195) 99.6195 m30 60sin 0.05  m2062650cos 0.0083.091 mh shh hssh s                       In this example, note that σβ was converted to radians (1 radian = 206265″) to balance the units in the relationship.   Calculating Positional and Survey Uncertainty for Terrestrial Observations 3   General Non-Linear Problems In the previous section we assumed that the measurements were independent. It is clear that when two different parameters are calculated with the same raw measurements then the results, and the error estimates will have some relationship to each other. For our purposes the obvious case is the calculation of co-ordinates from a radiation. It is clear that the Easting and Northing co-ordinates are calculated from the same distance and bearing observations so that errors in each of those observations will ‘show up’ in both co-ordinates but in a different way. Because they are connected in this way the two co-ordinates of a pair co-vary and so do the errors. So rather than talking about the variance we have to expand it to a 2 x 2 matrix called the variance co-variance matrix (VCV).     22TE ENNE NVCV J J               (4)       where the variances of the co-ordinate values are as before σEN = σNE is the covariance,  is a matrix of variances and J is the Jacobian matrix of the partial derivatives. In the case of the two dimensional co-ordinate pair the change in the co-ordinate values from a radiation are given as   sin cosE hN h             (5) where h is the horizontal distance and θ is the bearing. In this case the measurement of distance and bearing are independent so the variance matrix is only diagonal.   2200hE E N Nh h hVCVN N E Nh                                              (6) Combining Eqns (4) - (6)  2 22 2sin cos sin cos0cos sin cos sin0h E ENNE NhVCVh h h                                 (7) While the variances and covariances are useful measures of the precision of the co-ordinates it can be proved that the errors are more correctly represented by an ellipse which is based on the geometry of the survey observation rather than the co-ordinate system.  The maximum standard deviation will be the semi-major axis (a) of the ellipse and the minimum standard deviation the semi-minor axis (b). The orientation of the semi-major axis is given by the bearing Φ. Calculating Positional and Survey Uncertainty for Terrestrial Observations 4    Figure 1 The error ellipse parameters This is of far more use to surveyors so the next step is to determine the size of the semi-major and semi-minor axes, and the orientation of the ellipse from the VCV for the co-ordinate pair. Without proof, the size of the major and minor axes are calculated from eigenvalues of the square matrix.  1 221 and where and are the eigenvalues.ba      (The eigenvalues are those numbers that need to be subtracted from the variances so that the determinant of the VCV equals zero)  For the 2 x 2 matrix eigenvalues are given by the following formula, where λ1 is the maximum value which will give the semi-major axis of the ellipse.  2 2 2 2 2 21 1 ( ) 42 E N E N ENa                  (8)  2 2 2 2 2 22 1 ( ) 42 E N E N ENb                  (9)  The orientation of the major axis is given by the bearing Φ, which can be determined using the formulae: 2 22tan 2 ENN E             (10) The correct value of 2Φ is selected such that sin 2Φ has the same sign as σEN and cos 2Φ has the same sign as 2 2( )N E  .  Example 3 Compute the error ellipse for a single radiation. The horizontal distance is h = 200.0 with σh = 0.004 m and the bearing θ is 15°00' with σθ =3″   Calculating Positional and Survey Uncertainty for Terrestrial Observations 5   22226 6sin cos sin cos0cos sin cos sin00.004 00.258819 193.18517 0.258819 0.96592630.965926 51.76381 193.18517 51.7638102062658.97x10 1.88x10hhVCVh h h                                         6 51.88x10 1.55x10     0.00296 0.00394E N    2 2 2 2 2 21 ( ( ) 4 ) 0.0042 E N E N NEa            2 2 2 2 2 21 ( ( ) 4 0.00292 E N E N NEb            2 22tan 2 0.5776552 30.015ENN E       Converting Error Ellipses to SU ICSM’s Special Publication 1 (SP1) provides a method to convert an error ellipse to a circular confidence region. The SU can be calculated from the standard (1 sigma) error ellipse using; 2 3  *  1.960798  0.004071  0.114276   0.37165 ][ b b bSU aa a a                  (11) where a and b are the semi-major and semi-mior axes as before.    Example 4 Using the result of Example 3 calculate the SU for that point.  0.0040.00290.725abba      2 3 0.004 *  1.960798  0.004071 0.725 0.114276  0.725  0.37165 0.725 ]0.0086[SU         Calculating Positional and Survey Uncertainty for Terrestrial Observations 6   The Effect of Bearing on the Error Ellipse The previous calculations are clear cumbersome to perform for every line of the traverse. However if we examine the effect of the radiation bearing on the variances and SU we can take advantage of symmetry. The Table below shows the result of the previous calculations if we hold all measurements constant but just vary the bearing of the radiation.  Table 1 Table showing the variation in the error ellipse with the change in radiation bearing Bearing (°) σE σN a b  (°) 0 0.0029 0.0040 0.004 0.0029 0 10 0.0029 0.0040 0.004 0.0029 10 20 0.0031 0.0039 0.004 0.0029 20 30 0.0032 0.0038 0.004 0.0029 30 40 0.0034 0.0036 0.004 0.0029 40 50 0.0036 0.0034 0.004 0.0029 50 60 0.0038 0.0032 0.004 0.0029 60 70 0.0039 0.0031 0.004 0.0029 70 80 0.0040 0.0029 0.004 0.0029 80 90 0.0040 0.0029 0.004 0.0029 90     The table shows that for a single radiation the standard deviations on the co-ordinates vary with the radiation bearing but the ellipse maintains the same shape and merely rotates. This is a useful result as only the semi-major and semi-minor axes are used to calculate SU and they are invariant with the bearing.  Simplified SU Calculation  Figure 2 shows the relationship between the bearing and distance uncertainties and the resultant error ellipse.   A simplified calculation for SU for a single radiation is to calculate the axes using;       max , tan  min , tanhha hb h           (12)  and then substitute the results into Eqn. (11).   Calculating Positional and Survey Uncertainty for Terrestrial Observations 7    Figure 2 Figure showing the relationship between the error ellipse axes and radiation observations.  Example 5 Compute the error SU for a single radiation using the simplified method. The horizontal distance is h = 200.0 with σh = 0.004 m and the bearing θ is 15°00' with σθ =3″  2 30.004tan 200.tan(3") 0.0029 = 0.725 0.004 *  1.960798  0.004071 0.725 0.114276  0.725  0.37165 0.725 ]0.0086[hhabSU           Calculating PU for an Open Traverse  SP1 states a preference for calculating PU and SU as the result a rigorous least squares adjustment and there exist established approaches to adjust a closed traverse that are outside the scope of this paper.  Calculating Positional and Survey Uncertainty for Terrestrial Observations 8   For an open traverse however each leg of the traverse can be dealt with as if it is independent so the propagation of the error is as simple as Eqn. (1).   Example 6 Starting from PM123456 with a PU of 0.011 you traverse to a new point B via two traverse legs. PM-A θ is 65°00' h = 92.5 m, A-B θ is 142°00' h =60.35 m with σh = 0.003 m + 2ppm and with standard deviation of a single pointing σp 5″. The centring accuracy is σc = 0.002 m Compute PU of B. (Reading two faces) The bearing is the difference between two pointings of the total station so it is necessary to calculate the standard deviation of the bearing using Eqn. (1). The standard deviation of a mean measurement is  xssn  where s is the standard deviation of the sample and  n is the number of observations that are being meaned. In our case the n = 2 as two faces are being read and we use the instrument stated standard deviation for a single pointing.   2 2 2252p p ppn          Next calculate the SU for each leg.  Bearing (°) Distance σh h tan σθ SU 65 92.5 0.0032 0.0022 0.0068 142 60.35 0.0031 0.0015 0.0063   2 2 2 2 212 2 2 220.011 0.0068 0.0063 2 0.0020.0147nB i PM PM A A B ciPU PU SU SU          You will note that we have assumed that forced centring is being used so there are only two centring variances and the initial backsight is far enough away that its centring error does not contribute to the bearing uncertainty. If forced centring was not being used then there would have been four centring errors.   Acknowledgement Some worked examples, figures and text have been previously published in USQ study materials.     

Calculating positional and survey uncertainty for terrestrial observations

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Calculating positional and survey uncertainty for terrestrial observations

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